Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-27T10:04:36.817Z Has data issue: false hasContentIssue false

Optimal premium pricing policy in a competitive insurance market environment

Published online by Cambridge University Press:  21 August 2012

Athanasios A. Pantelous*
Affiliation:
Institute for Financial and Actuarial Mathematics (IFAM), Department of Mathematical Sciences, University of Liverpool, UK
Eudokia Passalidou
Affiliation:
Institute for Financial and Actuarial Mathematics (IFAM), Department of Mathematical Sciences, University of Liverpool, UK
*
*Correspondence to: Athanasios A. Pantelous, Institute for Financial and Actuarial Mathematics (IFAM), Department of Mathematical Sciences, University of Liverpool, L69 7ZL, Liverpool, UK. Tel. +44 151 79 45079. E-mail: A.Pantelous@liverpool.ac.uk

Abstract

In this paper, we propose a model for the optimal premium pricing policy of an insurance company into a competitive environment using Dynamic Programming into a stochastic, discrete-time framework when the company is expected to drop part of the market. In our approach, the volume of business which is related to the past year experience, the average premium of the market, the company's premium which is a control function and a linear stochastic disturbance, have been considered. Consequently, maximizing the total expected linear discounted utility of the wealth over a finite time horizon, the optimal premium strategy is defined analytically and endogenously. Finally, considering two different strategies for the average premium of the market, the optimal premium policy for a company with an expected decreasing volume of business is derived and fully investigated. The results of this paper are further evaluated by using data from the Greek Automobile Insurance Industry.

Type
Papers
Copyright
Copyright © Institute and Faculty of Actuaries 2012 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bertzekas, D.P. (2000). Dynamic programming and control theory. Athens Scientific Press, USA.Google Scholar
Daykin, C.D., Pentikäinen, T., Pesonen, M. (1994). Practical risk theory for actuaries. Chapman and Hall, USA.Google Scholar
Emms, P., Haberman, S. (2005). Pricing general insurance using optimal control theory. ASTIN Bulletin, 35(2), 427453.Google Scholar
Emms, P., Haberman, S., Savoulli, I. (2007). Optimal strategies for pricing general insurance. Insurance: Mathematics and Economics, 40(1), 1534.Google Scholar
Hellenic Association of Insurance Companies (2010). Statistical Tables (2006–2009), see http://www.eaee.gr/cms/Google Scholar
Jacobson, D.H. (1974). A General result in stochastic Optimal Control of Nonlinear Discrete-Time Systems with Quadratic Performance Criteria. Journal of Mathematical Analysis and Applications, 47, 153161.Google Scholar
Kushner, H.J. (1970). An introduction to stochastic control theory. John Wiley & Sons, USA.Google Scholar
Friedman, J. (1983). Oligopoly theory. Cambridge University Press, UK.Google Scholar
Ramana, B.V. (2006). Detariffication of Non-life Insurance Product Pricing in India: Impulses of Industry Experts vis-à-vis Perceptions of Public at Large (article). Insurance Chronicle Magazine: Reference # 11M-2006-12-08-01.Google Scholar
Taylor, G.C. (1986). Underwriting strategy in a competitive insurance environment. Insurance: Mathematics and Economics, 5(1), 5977.Google Scholar
Taylor, G.C. (1987). Expenses and underwriting strategy in competition. Insurance: Mathematics and Economics, 6(4), 275287.Google Scholar